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Greta Thunberg sailed the Atlantic to come the UN to talk about climate change. Her mother, a renowned opera singer, has given up air travel to support her daughter’s efforts. There is a zero-waste movement to lessen our trash and help alleviate the carbon footprints from our buying, traveling and more. These are steps humans have made. Yet technological advances may make it possible to flip the script for the environment and Big Data has a big role to play. What are Some of the Advances Taking Place? Technological advances have brought us breakthroughs in modern science and in every industry. Now, we are at a time and place in where our technologies cam help tackle climate change. From modeling to predictions, we can begin to build not just a map of environmental concerns, but begin to build a road toward a solution. Below are just a few of the ways technology is being used to advance solutions for climate change. AI modeling makes it easier to identify problemsPredictive Analytics models can create different scenarios to see ‘what happens if?’Big Data is used to identify areas which need immediate attention This is just the tip of the iceberg when it comes to using technology to predict and identify climate concerns. While some parts of the world contribute more to the problem than others, Big Data has made it possible to draw conclusions where the hardest hit areas are and is key to addressing the problem. But whatever Data brings, the information is useless if it isn’t used to formulate and put forward better environmental practices and policies. Ways to Upscale Urban Data Science Manhattan, Berlin, and New Delhi, as varied as they are, have one thing in common. They’re often sites for case studies when it comes to analyzing our environment. However, our advances continue to improve and we’re able to learn from state-of-the-art Data infrastructures. These can include such things as social media data combined with earth observations to see how they might better integrate. A research publication in Berlin suggest three routes for expanding knowledge. They are: Mainstream Data collectionsAmplify Big Data and Machine Learning to scale solutions and maintain privacyUse computational methods to analyze qualitative Data With these advances in place, there is a chance urban climate solutions could effect change on a global scale. With the proper Data of urban areas in place, including that of related greenhouse gases, socio-economic issues, and climate threats, Data professionals can get a clearer picture of what needs to be done. Building on the advances that are in place with the integrated technologies of AI, Predictive Analytics, and Big Data helps make big strides in combatting climate change. According to reports, only about 100 cities make up 20% of the global carbon footprint. Yet 97% of climate concerns are focused in urban areas. There’s still a lot which remains to be done to combat the greatest issue of our age, but working hand in hand – machine and human – we just might find ourselves on reprieve and the chance to leave the world better than we found it for the next generation. The next Greta Thunbergs of the world. If you’re interested in Big Data & Analytics, we may have a role for you. Check out our current opportunities or get in touch with one of our expert consultants to learn more. For our West Coast Team, call (415) 614 - 4999 or send an email to sanfraninfo@harnham.com. For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to newyorkinfo@harnham.com.
by Thomas Daughtrey
03. October 2019
Dream teams from sports to business are an ideal everyone aspires to live up to. But what is it every basketball or football dynasty has which makes them a dream team? What is it that brings individuals together to overcome odds, set examples, find solutions, and create the next best thing? Good management. The need for good management is no different in the Data Science world. Yet according to our latest Salary Guide, poor management is one of the top five reasons Data professionals leave companies. So, let’s take a look at what poor management is, what causes it, and how businesses can better retain Data talent. What’s Your Data Science Strategy? Most businesses know they need a Data team. They may also assume that a Data Scientist who performed well can lead a Data team. But that isn’t necessarily the case. Managers have to know things like P&L statements, how to build a business case, make market assessments, and how to deal with people. And that’s just for a start. The leader of a Data team has a number of other factors to consider as well such as Data Governance, MDM, compliance, legal issues around the use of algorithms, and the list goes on. At the same time, they also need to be managing their team with trust, authenticity, and candor. The list of responsibilities can be daunting and if someone is given too much too soon and without support, it can be a recipe for disaster. Other businesses might believe that a top performing Data Scientist would make a good manager. Yet these are two different fields. Or you might look at it this way. If you are willing to upskill a top performing Data professional and train them in managerial skills, giving them the education and support they need, that is one solution. Another solution is to create a Data Science strategy which brings in people with business backgrounds. Data Science is a diverse field and people come from a number of backgrounds not just Computer Science or Biostatistics, for example. Now that you’ve seen what might cause a manager to fail, let’s take a look at a few tips to help you succeed. Seven Tips for Managing a Data Team Managing a team is about being able to hire, retain, and develop great talent. But if the manager has no management training, well, that’s how things tend to fall apart. Here a few tips to consider to help ensure you and your team work together to become the dream team of your organization: Build trust by caring about your team. Help define their role within the organization. Ensure projects are exciting and that they’re not being asked to do project with vague guidelines or unrealistic timeframes.Be open and candid. Remember, Data Scientists are trained in how to gather, collect, and analyze information. If anyone can see right through a façade, it will be these Data professionals. Have those “tough” conversations throughout every stage of the hiring, onboarding, and day-to-day, so that no one is caught unaware.Offer consistent feedback. And ask for it for yourself as well from your team.Ensure your team understands the business goals behind their projects. Let them in on the bigger picture. Think long-term recruitment for a permanent role, not short-term. If you have an urgent project, consider contracting it out. Prioritize diversity to include academic discipline and professional experience. Does the way this person view the world expand the knowledge of your team’s knowledge? Dream teams don’t always have to agree. Sometimes, the best solutions are found when there are other opinions. Finding the perfect, “Full Stack” Data Scientist or Data Engineer or Analyst is not impossible, and retaining them can be even easier. If you’ve done your job well, your team will trust you, have a balanced skillset, and understand how their work supports the organization and its goals. For more information on how to be a great manager, check out this article from HBR. Ready for the next step? Check out our current vacancies or contact one of our recruitment consultants to learn more. For our West Coast Team, call (415) 614 - 4999 or send an email to sanfraninfo@harnham.com. For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to newyorkinfo@harnham.com.
11. July 2019
From startup and small business to large enterprises, each type of business requires a unique blend of Data professional. Though in today’s world, much of the Data being gathered, catalogued, and analyzed happens both in the Cloud and on a hard drive, each type of business has a different need, budget, goals, and objectives. But there is one thing each and every business will have in common. At the heart of the Data team will be a Data Engineer. The Three Main Roles of a Data Engineer This is an analytics role in high demand. It is a growing and lucrative field with steps and stages for nearly every level of business and education experience. For example, a Data Scientist interested in stepping into a Data Engineer role might begin as a Generalist. In all, there are three main roles for each level and type of business – Generalist, Pipeline-Centric, and Data-Centric. Let’s take a quick look at each of the roles with an eye toward the type of person who might be the best fit: Generalist – Most often found on a small team, this type of Data Engineer is most likely the only Data-focused person in the company. They may have to do everything from build the system to analyze it, and while it carries its own unique set of skills, it doesn’t require heavy architecture knowledge as smaller companies may not yet be focusing on scale. In a nutshell, this might be a good entry point for a Data Scientist interested in upskilling and reskilling themselves to transition into a Data Engineering role.Pipeline-centric – This focus requires more in-depth knowledge working with more complex Data science needs. This type of role is found more often in mid-sized companies as they grow and incorporate a team of Data professionals to help analyze and offer actionable insight for the business. In a nutshell, this role creates a useful format for analysts to gather, collect, and analyze each bit of Data at each stage of development.Database-centric – This role is found most often in larger companies and deals not only with Data warehouses, but is focused on setting up analytics databases. Though there are some elements of the pipeline, this is more fine-tuned. In a nutshell, this role deals with many analysts across a wide distribution of databases. A Fine Balance Between Technical Skills, Soft Skills, and Business Acumen While it’s important for anyone filing this role to have deep knowledge of database design as well as a variety of programming languages, its equally important to understand company objectives. In other words, once the groundwork is laid and the datasets established, it’ll be important to explain what it is the business executives need to know to make the best decisions for their business. Knowing how and what to communicate to executives, stakeholders, and your Data team also means understanding how to best retrieve and optimize the information for reporting. Depending on your organization’s size, you may need both a Data Analyst or Scientist and a Data Engineer. Though this is less likely in medium and larger enterprises. On the flip side, in order to understand the business’ needs, you’ll also need to be good at creating reliable pipelines, architecting systems and Data stores, and collaborating with your Data Science team to build the right solutions. Each of these skills are meant to help you understand concepts to build real-world systems no matter the size of your business. One Final Thought… Do you like to build things? Tweak systems? Take things apart and see how they work, then put them back together better and more efficient than before? Then Data Engineering might be for you. Are you a business who knows you’re ready to scale up and hire a Data professional? We have a strong candidate pool and may have just the person you need to fill your role. Are you a candidate looking for a role in big Data and analytics? We specialize in junior and senior roles. Check out our current vacancies or contact one of our recruitment consultants to learn more. For our West Coast Team, call (415) 614 - 4999 or send an email to sanfraninfo@harnham.com. For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to newyorkinfo@harnham.com.
11. June 2019
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